{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze Big Financial Data**\n",
    "\n",
    "O'Reilly (2014)\n",
    "\n",
    "Yves Hilpisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"border:0px solid grey;\" src=\"http://hilpisch.com/python_for_finance.png\" alt=\"Python for Finance\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Buy the book ** |\n",
    "<a href='http://shop.oreilly.com/product/0636920032441.do' target='_blank'>O'Reilly</a> |\n",
    "<a href='http://www.amazon.com/Yves-Hilpisch/e/B00JCYHHJM' target='_blank'>Amazon</a>\n",
    "\n",
    "**All book codes & IPYNBs** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**The Python Quants GmbH** | <a href='http://pythonquants.com' target='_blank'>www.pythonquants.com</a>\n",
    "\n",
    "**Contact us** | <a href='mailto:analytics@pythonquants.com'>analytics@pythonquants.com</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normality Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark Case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "uuid": "b5c2a3e0-81d2-4aab-bee0-a9239fc9ffa6"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(1000)\n",
    "import scipy.stats as scs\n",
    "import statsmodels.api as sm\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "uuid": "596ccb67-e163-4f15-95e8-80362bdade98"
   },
   "outputs": [],
   "source": [
    "def gen_paths(S0, r, sigma, T, M, I):\n",
    "    ''' Generate Monte Carlo paths for geometric Brownian motion.\n",
    "    \n",
    "    Parameters\n",
    "    ==========\n",
    "    S0 : float\n",
    "        initial stock/index value\n",
    "    r : float\n",
    "        constant short rate\n",
    "    sigma : float\n",
    "        constant volatility\n",
    "    T : float\n",
    "        final time horizon\n",
    "    M : int\n",
    "        number of time steps/intervals\n",
    "    I : int\n",
    "        number of paths to be simulated\n",
    "        \n",
    "    Returns\n",
    "    =======\n",
    "    paths : ndarray, shape (M + 1, I)\n",
    "        simulated paths given the parameters\n",
    "    '''\n",
    "    dt = float(T) / M\n",
    "    paths = np.zeros((M + 1, I), np.float64)\n",
    "    paths[0] = S0\n",
    "    for t in range(1, M + 1):\n",
    "        rand = np.random.standard_normal(I)\n",
    "        rand = (rand - rand.mean()) / rand.std()\n",
    "        paths[t] = paths[t - 1] * np.exp((r - 0.5 * sigma ** 2) * dt +\n",
    "                                         sigma * np.sqrt(dt) * rand)\n",
    "    return paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "uuid": "7b6ba027-7f3c-43ee-a297-66599a4c9ac2"
   },
   "outputs": [],
   "source": [
    "S0 = 100.\n",
    "r = 0.05\n",
    "sigma = 0.2\n",
    "T = 1.0\n",
    "M = 50\n",
    "I = 250000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "uuid": "abdf423c-dc32-4528-8781-12e05a6703cd"
   },
   "outputs": [],
   "source": [
    "paths = gen_paths(S0, r, sigma, T, M, I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "uuid": "133e6168-692d-4aa7-8b8d-81d21e1d21e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5162835f10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AkxTtEhzS+QBUAfAUH9aaSNEuwzdHJmey98Fett7ZWqtzVa0mLSzIxYuT779wgaxaVYRg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f516287ac10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(paths[:, :10])\n",
    "plt.grid(True)\n",
    "plt.xlabel('time steps')\n",
    "plt.ylabel('index level')\n",
    "# tag: normal_sim_1\n",
    "# title: 10 simulated paths of geometric Brownian motion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "uuid": "c9a7ad7d-921d-4b03-a81e-bff7d6e60b8e"
   },
   "outputs": [],
   "source": [
    "log_returns = np.log(paths[1:] / paths[0:-1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "uuid": "4fe20e49-2c58-454c-a086-14c2f2901dba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 100.    ,   97.821 ,   98.5573,  106.1546,  105.899 ,   99.8363,\n",
       "        100.0145,  102.6589,  105.6643,  107.1107,  108.7943,  108.2449,\n",
       "        106.4105,  101.0575,  102.0197,  102.6052,  109.6419,  109.5725,\n",
       "        112.9766,  113.0225,  112.5476,  114.5585,  109.942 ,  112.6271,\n",
       "        112.7502,  116.3453,  115.0443,  113.9586,  115.8831,  117.3705,\n",
       "        117.9185,  110.5539,  109.9687,  104.9957,  108.0679,  105.7822,\n",
       "        105.1585,  104.3304,  108.4387,  105.5963,  108.866 ,  108.3284,\n",
       "        107.0077,  106.0034,  104.3964,  101.0637,   98.3776,   97.135 ,\n",
       "         95.4254,   96.4271,   96.3386])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paths[:, 0].round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "uuid": "cd6ee063-9463-405d-96bd-2ad1231d7e7f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.022 ,  0.0075,  0.0743, -0.0024, -0.059 ,  0.0018,  0.0261,\n",
       "        0.0289,  0.0136,  0.0156, -0.0051, -0.0171, -0.0516,  0.0095,\n",
       "        0.0057,  0.0663, -0.0006,  0.0306,  0.0004, -0.0042,  0.0177,\n",
       "       -0.0411,  0.0241,  0.0011,  0.0314, -0.0112, -0.0095,  0.0167,\n",
       "        0.0128,  0.0047, -0.0645, -0.0053, -0.0463,  0.0288, -0.0214,\n",
       "       -0.0059, -0.0079,  0.0386, -0.0266,  0.0305, -0.0049, -0.0123,\n",
       "       -0.0094, -0.0153, -0.0324, -0.0269, -0.0127, -0.0178,  0.0104,\n",
       "       -0.0009])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns[:, 0].round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "uuid": "77290ae6-4035-42a6-8312-ec89da50f88b"
   },
   "outputs": [],
   "source": [
    "def print_statistics(array):\n",
    "    ''' Prints selected statistics.\n",
    "    \n",
    "    Parameters\n",
    "    ==========\n",
    "    array: ndarray\n",
    "        object to generate statistics on\n",
    "    '''\n",
    "    sta = scs.describe(array)\n",
    "    print \"%14s %15s\" % ('statistic', 'value')\n",
    "    print 30 * \"-\"\n",
    "    print \"%14s %15.5f\" % ('size', sta[0])\n",
    "    print \"%14s %15.5f\" % ('min', sta[1][0])\n",
    "    print \"%14s %15.5f\" % ('max', sta[1][1])\n",
    "    print \"%14s %15.5f\" % ('mean', sta[2])\n",
    "    print \"%14s %15.5f\" % ('std', np.sqrt(sta[3]))\n",
    "    print \"%14s %15.5f\" % ('skew', sta[4])\n",
    "    print \"%14s %15.5f\" % ('kurtosis', sta[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "uuid": "57ac0ad6-14c7-4158-b67b-b553d662dc21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     statistic           value\n",
      "------------------------------\n",
      "          size  12500000.00000\n",
      "           min        -0.15664\n",
      "           max         0.15371\n",
      "          mean         0.00060\n",
      "           std         0.02828\n",
      "          skew         0.00055\n",
      "      kurtosis         0.00085\n"
     ]
    }
   ],
   "source": [
    "print_statistics(log_returns.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "uuid": "1bb98c2c-002b-4e19-88af-ae23db1a673f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f515065ed10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c3JBEoLk20p459PPGf/Ebnqg0233XBC7mcjFTGGIb+0hEbgG2qmrak1FLS0spKioCoLCw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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5162835890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(log_returns.flatten(), bins=70, normed=True, label='frequency')\n",
    "plt.grid(True)\n",
    "plt.xlabel('log-return')\n",
    "plt.ylabel('frequency')\n",
    "x = np.linspace(plt.axis()[0], plt.axis()[1])\n",
    "plt.plot(x, scs.norm.pdf(x, loc=r / M, scale=sigma / np.sqrt(M)),\n",
    "         'r', lw=2.0, label='pdf')\n",
    "plt.legend()\n",
    "# tag: normal_sim_2\n",
    "# title: Histogram of log-returns and normal density function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "uuid": "75c069e5-c518-4fca-8b60-de465ba1a56b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f515c5f1bd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m8AzHs3z5DcBWevdumMNRcRizzQXFIUGxSIhDLNTTqENbtiSHN9HT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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515c521090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(log_returns.flatten()[::500], line='s')\n",
    "plt.grid(True)\n",
    "plt.xlabel('theoretical quantiles')\n",
    "plt.ylabel('sample quantiles')\n",
    "# tag: sim_val_qq_1\n",
    "# title: Quantile-quantile plot for log returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "uuid": "a6e5cdd2-0e90-4b34-8c13-d52dfe7e897b"
   },
   "outputs": [],
   "source": [
    "def normality_tests(arr):\n",
    "    ''' Tests for normality distribution of given data set.\n",
    "    \n",
    "    Parameters\n",
    "    ==========\n",
    "    array: ndarray\n",
    "        object to generate statistics on\n",
    "    '''\n",
    "    print \"Skew of data set  %14.3f\" % scs.skew(arr)\n",
    "    print \"Skew test p-value %14.3f\" % scs.skewtest(arr)[1]\n",
    "    print \"Kurt of data set  %14.3f\" % scs.kurtosis(arr)\n",
    "    print \"Kurt test p-value %14.3f\" % scs.kurtosistest(arr)[1]\n",
    "    print \"Norm test p-value %14.3f\" % scs.normaltest(arr)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "uuid": "d35f18c9-d798-47f7-85d4-dc09b0907134"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew of data set           0.001\n",
      "Skew test p-value          0.430\n",
      "Kurt of data set           0.001\n",
      "Kurt test p-value          0.541\n",
      "Norm test p-value          0.607\n"
     ]
    }
   ],
   "source": [
    "normality_tests(log_returns.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "uuid": "80629df6-776e-4849-872b-46b6b35d4eb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f515c373cd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jYnvgd4EbI2LPKjR4zMzMzKC7Rs+bIuL6xkBE3AC8ubiQDNwnO2hV3N5VjNnK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\nB5+e0byBYm49AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515c48d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4))\n",
    "ax1.hist(paths[-1], bins=30)\n",
    "ax1.grid(True)\n",
    "ax1.set_xlabel('index level')\n",
    "ax1.set_ylabel('frequency')\n",
    "ax1.set_title('regular data')\n",
    "ax2.hist(np.log(paths[-1]), bins=30)\n",
    "ax2.grid(True)\n",
    "ax2.set_xlabel('log index level')\n",
    "ax2.set_title('log data')\n",
    "# tag: normal_sim_3\n",
    "# title: Histogram of simulated end-of-period index levels\n",
    "# size: 90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "uuid": "9e7b6096-9d21-4199-882b-b38f760fc72e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     statistic           value\n",
      "------------------------------\n",
      "          size    250000.00000\n",
      "           min        42.74870\n",
      "           max       233.58435\n",
      "          mean       105.12645\n",
      "           std        21.23174\n",
      "          skew         0.61116\n",
      "      kurtosis         0.65182\n"
     ]
    }
   ],
   "source": [
    "print_statistics(paths[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "uuid": "b9b2eab0-7788-48f7-b4b2-c3f1e263f0b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     statistic           value\n",
      "------------------------------\n",
      "          size    250000.00000\n",
      "           min         3.75534\n",
      "           max         5.45354\n",
      "          mean         4.63517\n",
      "           std         0.19998\n",
      "          skew        -0.00092\n",
      "      kurtosis        -0.00327\n"
     ]
    }
   ],
   "source": [
    "print_statistics(np.log(paths[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "uuid": "7bd3a6dc-ca9f-4878-bf30-27127547b952"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew of data set          -0.001\n",
      "Skew test p-value          0.851\n",
      "Kurt of data set          -0.003\n",
      "Kurt test p-value          0.744\n",
      "Norm test p-value          0.931\n"
     ]
    }
   ],
   "source": [
    "normality_tests(np.log(paths[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "uuid": "fbe45821-3fda-4924-9b65-b7aae6004ae6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f515c0dee50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kj7ffBmACgygkdrXwVPMpQIsWsHkzLDmm4msSWMRTZ/vJykcmJlShfXvYtImf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515c354910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_data = np.log(paths[-1])\n",
    "plt.hist(log_data, bins=70, normed=True, label='observed')\n",
    "plt.grid(True)\n",
    "plt.xlabel('index levels')\n",
    "plt.ylabel('frequency')\n",
    "x = np.linspace(plt.axis()[0], plt.axis()[1])\n",
    "plt.plot(x, scs.norm.pdf(x, log_data.mean(), log_data.std()),\n",
    "         'r', lw=2.0, label='pdf')\n",
    "plt.legend()\n",
    "# tag: normal_sim_4\n",
    "# title: Histogram of log index levels and normal density function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "uuid": "1db3cb57-4537-4e8c-96ca-9a96cb35bf99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f515c13c510>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W/51hrf8NqvUvEimaExBAtf5FpGkZnxMws5ZmNtfMHm3i9fFm9pqZzTezgzId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\n2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515c4963d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(log_data, line='s')\n",
    "plt.grid(True)\n",
    "plt.xlabel('theoretical quantiles')\n",
    "plt.ylabel('sample quantiles')\n",
    "# tag: sim_val_qq_2\n",
    "# title: Quantile-quantile plot for log index levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real World Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "uuid": "2fac84d1-67fd-4345-a9b8-58e8e20b6ea6"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas.io.data as web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "uuid": "8c2cddba-d3fc-4e22-8b22-b0a6dcf61f16"
   },
   "outputs": [],
   "source": [
    "symbols = ['^GDAXI', '^GSPC', 'YHOO', 'MSFT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "uuid": "eaa5651f-3c91-4f0f-9941-b564e4b7dbe1"
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame()\n",
    "for sym in symbols:\n",
    "    data[sym] = web.DataReader(sym, data_source='yahoo',\n",
    "                            start='1/1/2006')['Adj Close']\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "uuid": "e4574de5-c00f-4665-b341-dad771d24d8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2390 entries, 2006-01-03 to 2015-08-07\n",
      "Data columns (total 4 columns):\n",
      "^GDAXI    2390 non-null float64\n",
      "^GSPC     2390 non-null float64\n",
      "YHOO      2390 non-null float64\n",
      "MSFT      2390 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 93.4 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "uuid": "f2acb16d-fd16-4008-95a7-5213e3df3a9e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>^GDAXI</th>\n",
       "      <th>^GSPC</th>\n",
       "      <th>YHOO</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-03</th>\n",
       "      <td>5460.680176</td>\n",
       "      <td>1268.800049</td>\n",
       "      <td>40.910000</td>\n",
       "      <td>21.659681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-04</th>\n",
       "      <td>5523.620117</td>\n",
       "      <td>1273.459961</td>\n",
       "      <td>40.970001</td>\n",
       "      <td>21.764590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05</th>\n",
       "      <td>5516.529785</td>\n",
       "      <td>1273.479980</td>\n",
       "      <td>41.529999</td>\n",
       "      <td>21.780730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-06</th>\n",
       "      <td>5536.319824</td>\n",
       "      <td>1285.449951</td>\n",
       "      <td>43.209999</td>\n",
       "      <td>21.716170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-09</th>\n",
       "      <td>5537.109863</td>\n",
       "      <td>1290.150024</td>\n",
       "      <td>43.419998</td>\n",
       "      <td>21.675821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ^GDAXI        ^GSPC       YHOO       MSFT\n",
       "Date                                                      \n",
       "2006-01-03  5460.680176  1268.800049  40.910000  21.659681\n",
       "2006-01-04  5523.620117  1273.459961  40.970001  21.764590\n",
       "2006-01-05  5516.529785  1273.479980  41.529999  21.780730\n",
       "2006-01-06  5536.319824  1285.449951  43.209999  21.716170\n",
       "2006-01-09  5537.109863  1290.150024  43.419998  21.675821"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "uuid": "d4cf7b80-4be5-48d6-b669-acf6c9784941"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f515bfc4d90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V9EGhT9zV/1QT9Kpdr7pBv9r1qhuaXnuy0cjStLQK208bjbS3DEubhw3juvBwioUgs7SU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515c421750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(data / data.ix[0] * 100).plot(figsize=(8, 6), grid=True)\n",
    "# tag: real_returns_1\n",
    "# title: Evolution of stock and index levels over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "uuid": "8caf3129-34cb-4ce0-a53b-bef84d3db2dc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>^GDAXI</th>\n",
       "      <th>^GSPC</th>\n",
       "      <th>YHOO</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-04</th>\n",
       "      <td>0.011460</td>\n",
       "      <td>0.003666</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>0.004832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05</th>\n",
       "      <td>-0.001284</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.013576</td>\n",
       "      <td>0.000741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-06</th>\n",
       "      <td>0.003581</td>\n",
       "      <td>0.009356</td>\n",
       "      <td>0.039656</td>\n",
       "      <td>-0.002968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-09</th>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>-0.001860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ^GDAXI     ^GSPC      YHOO      MSFT\n",
       "Date                                              \n",
       "2006-01-03       NaN       NaN       NaN       NaN\n",
       "2006-01-04  0.011460  0.003666  0.001466  0.004832\n",
       "2006-01-05 -0.001284  0.000016  0.013576  0.000741\n",
       "2006-01-06  0.003581  0.009356  0.039656 -0.002968\n",
       "2006-01-09  0.000143  0.003650  0.004848 -0.001860"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns = np.log(data / data.shift(1))\n",
    "log_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "uuid": "f6fefbe1-3ec6-4d1b-b774-9db3f3c38c83"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f515b903e90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f515a0771d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5159ffc150>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5159fd3f50>]], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jpffnyJiZjTKfdW2nWnJIJB0naaOkOyWdWUcZc9V0H9vo5JDMsuSW9qs2fby1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\nbiW1GxKLFy9uugo921HXTlw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COCkitqZ1VgJvA34GnBoRaybZruNNRZzUalMZSFKrpMuA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\nMTMzs8a5QWJmZmaNc4PEzMzMGvf/Axvjgx/hhJYzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515b99c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_returns.hist(bins=50, figsize=(9, 6))\n",
    "# tag: real_returns_2\n",
    "# title: Histogram of respective log-returns\n",
    "# size: 90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "uuid": "5e6f48e5-68f2-44ec-ad92-635ad3c249ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Results for symbol ^GDAXI\n",
      "------------------------------\n",
      "     statistic           value\n",
      "------------------------------\n",
      "          size      2389.00000\n",
      "           min        -0.07739\n",
      "           max         0.10797\n",
      "          mean         0.00031\n",
      "           std         0.01452\n",
      "          skew         0.01524\n",
      "      kurtosis         6.11362\n",
      "\n",
      "Results for symbol ^GSPC\n",
      "------------------------------\n",
      "     statistic           value\n",
      "------------------------------\n",
      "          size      2389.00000\n",
      "           min        -0.09470\n",
      "           max         0.10957\n",
      "          mean         0.00021\n",
      "           std         0.01321\n",
      "          skew        -0.31964\n",
      "      kurtosis        10.43619\n",
      "\n",
      "Results for symbol YHOO\n",
      "------------------------------\n",
      "     statistic           value\n",
      "------------------------------\n",
      "          size      2389.00000\n",
      "           min        -0.24636\n",
      "           max         0.39182\n",
      "          mean        -0.00005\n",
      "           std         0.02552\n",
      "          skew         0.54866\n",
      "      kurtosis        32.53108\n",
      "\n",
      "Results for symbol MSFT\n",
      "------------------------------\n",
      "     statistic           value\n",
      "------------------------------\n",
      "          size      2389.00000\n",
      "           min        -0.12458\n",
      "           max         0.17063\n",
      "          mean         0.00032\n",
      "           std         0.01776\n",
      "          skew         0.04323\n",
      "      kurtosis        10.32258\n"
     ]
    }
   ],
   "source": [
    "for sym in symbols:\n",
    "    print \"\\nResults for symbol %s\" % sym\n",
    "    print 30 * \"-\"\n",
    "    log_data = np.array(log_returns[sym].dropna())\n",
    "    print_statistics(log_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "uuid": "83fc3d8b-03c4-43bc-a7a3-b7ac0747c2b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5159a76f90>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SO4GzgN/g1pwyAWnp/crpFoacYcgILS/npEnT2LjxAK5rqi1QAGyIu0W6qqYDpbRv/y2u\nu+6k5ApHTY3rirrmGjj+ePjSl+DFF6kcNgzeew9eew1mzoQvfznrCodfflJ/CzgJWKKql3kLEz6a\n2ljGGBOsSZOmMXfuCtyCGZHZUz1wLY2HcYWjFlgLvEl+fheOO64bN988penCoQqrV0dbFy+9BCNG\nuJbFn/8Mo0dDmzZQWQk9e6boFaaXn/M8XlPVk0XkDdwo0g5glaoe1+gDs4B1Wxljysqq+O53p7N9\nezdcS6Mt9ZfW60VsF1WvXj/jvvu+3nTBqK52XVDz5rmCUVsb7Yo65xzo1i34F5MmQZ3n8ZqIdAPu\nBV7HjR69EkA+Y4xJKdfaWIK7DNFIYCXuTPGHcGuubgDWAReQm5vPsGFHcvPNFycuHKqwYkW0dfHq\nqzBmjCsWf/sbDB8O0iKuEuFLk2MeqvoTVf1cVf+Ea99dqiG4fnmYtLR+5UwLQ84wZIRw5xw37jLm\nzn0bOALXPVUHjAMeA74P9AZOBjpRXHwCe/Y8wRtv/LF+4fj8c3jySbjiCjcL6sIL3eyoq692U2xf\neAF+/nM48URfhSMs76cfTbY8RGQcccuni8hYVa1KWSpjjGmGSZOmUVW1GTgS18LYQ3QW1QjgLtzk\n0S0UFw/nsce8K2AfOABLlriWxbx5sHw5nHmma11Mm+ZmSbWi1kVj/Ix5PEe0eOTilj5/o7kLI6aD\njXkY0/q4rqq3cMuL1OA+vsYBy4FJRNemWkZx8XE8dse1bmZUebn7euSR0bGLM8+EvLyMvZZMCew8\nj7gn7Q/MUtVvNCdcOljxMKblKyur4oorbmXTps9wa7f2xnVT7cHNrPoMV0hGAFXk0J5TWM2V/Xdz\nac92bpbU2WdHT9Lzez2LFiyQ8zwS+Bg4/vAimUTC0g9qOYMThoyQ/TnLyqr4xjduYtOmjUB34Chc\nV9UeXGtjB9CNvqzjcu7hL7zMFubyaMEHXPrd8+F3v4MtW+Dpp+HKK1NeOLL9/UyGnzGP2GuYt8FN\nWcj65diNMS3flCn3sXdvR9z14bp6e2tpz+mcwd8pooYi5tGHWio4ioUde9DtT7M553tfz2DqlsHP\nmMfkmM06YK2qvpTKUEGxbitjWqbouRvdcRcihaPZRxHvUcQOCtnIO/ShnHzKGchrCD2O2sfGjfMz\nGzwkUjLmESZWPIxpWdxg+AKgI3l0pJAdFLGJIrbQhf2U05NyerOAwXzGMUQu2tSmzds888zVwayC\n2woEtbbVWyKy3Psaf1seXNzWKyz9oJYzOGHICNmTc9KkaYicwNK5/+BqdjGfd9nEQqaxmg205Xz6\n0IexXMYpPEEfPmMbbnbVO4i8wfTp52RF4ciW9zMIfs4wL8fNdfszbsJ05IK9d3nbxhgTuLKyKq64\n6FpO27OJImq5jR0IMI9e3M1xXEQXdtAH9/G0HDeragfujIJ8oC2DB+cxa9ZVWVE4Who/Yx7LVHVk\n3L6lqjoqpckCYN1WxoTMgQP88vzLYX45RexiNLt5hW6U04NyBrCKAmC3d3BkWi5AHyJdVLCC5577\nmRWMZghqbSsRkTMig+Qi8mWsxWGMCUrkOt3l5Wx6+DEu0zaU05Pb6ccijmA3XXEfOdW4wrEL99HV\nBtgJdAHexw2cf0px8QlWONLAz3kelwN3icg6EVmH6666PLWxWpew9INazuCEISOkKGeC63Qv/NHP\n+MlDFZyup3EcZzGVk5nH0eymAFcsqnGtih3e1y647qqPgU+ATxDZzIwZ50eXGslCYfm9+9Fky0NV\n3wBGiEiBt70t5amMMS1Lgut0v9z5KH61eDMv0Y+95ONO7uvkPaAGd2aAEC0afXEFYxtuJdwjvX27\nOPvsPrzwwtx0v6pWzabqGmOC18B1uv9W24GfPvs662lL5PwMd1Y4uMJR7d2vxXVRCa5A7AC24loc\nnYF25OfX8sQT06yLKgXsPA8rHsakTwPX6f7ps29wz5vr2I/gBrVzcGtPRXrNI62NamA7sA93xngP\nYD2ukESLRrduB/jzn6+xopFCqVrbygQsLP2gljM4YcgITeRs4DrdfPObXHh8EfL6HuSWp7nrzY/Y\nTxtcC6IPrqXRGVc0Iq2Nalx3VBfcNNtI11QBMBjIo7h4GKpP8dln/3tI4WgR72fI+FnbKh+4Bhig\nqj8UkSHAcar6XMrTGWOyRyPX6f5R3he4b2sb9NFl8Ogy7wEC9It5gnyiEzWrY/Zvw41vDMQNgEe6\nqjoCOykuPiarB8FbKz/nefwFtxDipap6gldMXlHVk9IRsDms28qYZmrgOt1/2SFc+deX2UZOggf1\nj7nfKeZ+fMGo9e4fgysae4BuRLqncnN389e/Xm/dUxkQyJiHiLyhql+MPTFQRN604mFMC9TAdbqv\nr3qXZ+tyWUEHDj3Nq3/cdmMFYzfRcY/uuGuIH/DuW9HIFkGNedSKyMFLaYnIYKJ/MpgAhKUf1HIG\nJ6syJrhO98M3z+Y//vE+edXdkH9s4ra6AlaQS7RLqX/MrVPcrTrmtgnXqvgY1zXVGzcgXoMrHH2A\noUAeJ5zQFtWn2LOnPOnCkVXvZyPCktMPP2eYl+LWt+onIo8BXwYmpzCTMSaVElyne1nnnvz3ht2U\n04nVtMeNN4A7QS++ZRE7dgH1WxfgCsZe75hOQAfcmeB7cAWjH5Eic8IJuaxY8WSgL8+kh6+puiJy\nJHCqt/kvVd2a0lQBsW4rYzybNx+8Tnftc2Ws2b6HcjpRTidepCM19TohGuuGgoaLRURn7+tO72t7\n3AwrVzDGju3FokUPNOPFmFRr1piHiHwRdzrnwV3eVwVQ1SVBhEwlKx6m1aqrg8WLueWMr1FENUPY\nyz/Ip5xOzKcT6w6eoAeuJRD7OdFUsdhGtDCAKw4dEuyzghFWzS0eldQvHvWo6lnNSpcGYSkelZWV\nFBYWZjpGkyxncILO2LbtCHrtr2UC1RRRzTlUs5b2B1sX/ySPfbTBnXiXG/foxorFp0RXsY3VDzeO\nERGZluuKxYABdaxbt6B5LyoJYfidQ3hyNmtVXVUtDDyRMaZZRIYfvN+eA5zBboqoZinV9KGOCvJ5\njs5MoRcbOSbBM+TBIdNr41sWHxP9u3EX0Iv6rYrIMZHB8mry8jaze/e8w3xVJoz8TNXNA34CnIH7\nF/UicLeq1qQ+XvOEpeVhTLzYIhHraPZS5LUuCtnFO3SgnD6U04PX6MqBRrufwA1a74/bt9Xbf/Cn\nA+2oP45h3VCtSVDneTyJW5XsEdy/qklAV1W9KICARcAduD+F7lPVQ04jFZHZwHm4tvNkVV2axGOt\neJis1abNcJr655nHAQrZ5RWMGrpQRzlHUk4PFtCdz2hP4iIBh7YowBWJRPNduuO6qGJFWxZdunzG\n9u2LGw9rWoygisc7qjqsqX2HES4HeBc4B7f62WtAsaqujDnmfOAqVT1fRE4BZqnqqX4e6z0+FMUj\nLP2gljN5DReIXbgpr/GUoRxBEVs5jy2cxjaW0MUrGAN4kwL0kJP0EhUJgI8a2D8UWJVgf7RYiHzE\ngQMrsuq9bIzlDFZQVxJcIiKnqeo/vSc9FbdcSXONAd5X1bXe8z4OXAjEFoCvAg8BqOpiESkQkV7A\n0T4ea0xalJbexU033ZXko3rg/tqHztTxFT6liC0UsRXh3951uo/lInqyg3beY6pxRSdeQ0XiNOCf\nCfavIrZQwEeorkgyv2nt/LQ8VgHH4v6FKjAA91d/HaCqOuKwfrDIt4AJqvpDb/sS4BRVLYk55lng\nNlV9xdt+HpgGDAKKGnustz8ULQ8TDgMHnsuHH25I4hHx50s4gnISdRSxiSI2MpptvEJ3yjmKcnqx\nCiHxlZ4bKhJNZbAiYZITVMujKKA88fx+qtv10k3aNTRgfai+NLzKT3Qs4ghqOZfNFLGRIjaygxzK\nOZLbGcgiRrK73n/FhoqE0Ph/m/jZT0F0EBiTmJ/L0K4VkW64f5ltY/Y39yTB9dT/06w/9SeOJzom\nMrm8nY/HAjB58mQGDRoEQEFBASNHjjzY5xhZZybT25F92ZKnoe077rgjK9+/5r6fZ511lfeoSJdQ\nfoLt/kQHlLt7Xz/FrdHU09ve7H3tSRuU43ibMWznx+xiGNXMoRNPU8BMTuEDPox7jtifF5kaG58n\nMrYxFFck1tGx40527VqZ1PuTzPayZcu4+uqrU/b8QW3H/+4znaeh7Wx9PysrK3nwwQcBDn5eNsVP\nt9VM3FpWH+CWvwSaf5KgiLTFdX99BXcF+1dpfMD8VOAOb8C8ycd6jw9Ft1VlSAbRWkLOceMuo6rq\ntSaeIVF3U9MzmnpRw3g+5Ty2cC6f8gltDp6k9xId2VuvhdLQgHlEdkyNbQm/82wSlpxBzbZ6Dxiu\nqnsbPfAwiMh5RKfb3q+qt4nIlQCqeo93zJ24rrNdwGWRFk+ixyZ4/lAUD5M6ZWVVXHDBTxo5wm+h\nOHRGU1sOcDofU8RmiqhmEHt53isW8+nE+oMD3Y2JdjXl5Kynrm65j8cYk1pBFY+ngR+r6qYgw6WD\nFY/WadKkacydW5bgO4dfKCJnXQ+IOUnvLHaxmvbeSXrKYvK863Qn+rlWIEx4BFU8Tgb+Bqwgeh0P\nVdWvBpIyhcJSPMLSlM3mnF27nsKOHZExgkiXUPw6Tn4LRXTAugMHGOstAXIe1RxJHfO91kUFndhy\ncBgwuVlN2fxexrKcwQpLzqBmWz0M/BpXPCJjHtn/iWxavJ49z2TLls/j9vYDPsMNasev49R4oXCU\nIShFfE4R1ZzBbpaTSzmd+B59WcIQlM7Y1FfT2vlpebymqienKU+gwtLyMP7Vb2FENHTN7Ph1nOKv\nOwEwlHze5ix2cx4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/mvGZVWHprw1DzjBkBMsZtLDk9MPOMM8ykeIwZ850ampyyM2FkpIf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\nHU5ylskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515b903d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(log_returns['^GSPC'].dropna(), line='s')\n",
    "plt.grid(True)\n",
    "plt.xlabel('theoretical quantiles')\n",
    "plt.ylabel('sample quantiles')\n",
    "# tag: real_val_qq_1\n",
    "# title: Quantile-quantile plot for S&P 500 log returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "uuid": "a6e1cee5-aebc-4c63-8ce4-100562f31859"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f51599a6a50>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xBgexu8U5RqtpmCSWpFWSMKa1/JStluAu8bUC9zVpCK5kVAOoqh6b6iD3lZWt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\n9JupOewBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5159956050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(log_returns['MSFT'].dropna(), line='s')\n",
    "plt.grid(True)\n",
    "plt.xlabel('theoretical quantiles')\n",
    "plt.ylabel('sample quantiles')\n",
    "# tag: real_val_qq_2\n",
    "# title: Quantile-quantile plot for Microsoft log returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "uuid": "bce2aa32-77f9-4d5a-9a81-1a37b6b90001"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Results for symbol ^GDAXI\n",
      "--------------------------------\n",
      "Skew of data set           0.015\n",
      "Skew test p-value          0.760\n",
      "Kurt of data set           6.114\n",
      "Kurt test p-value          0.000\n",
      "Norm test p-value          0.000\n",
      "\n",
      "Results for symbol ^GSPC\n",
      "--------------------------------\n",
      "Skew of data set          -0.320\n",
      "Skew test p-value          0.000\n",
      "Kurt of data set          10.436\n",
      "Kurt test p-value          0.000\n",
      "Norm test p-value          0.000\n",
      "\n",
      "Results for symbol YHOO\n",
      "--------------------------------\n",
      "Skew of data set           0.549\n",
      "Skew test p-value          0.000\n",
      "Kurt of data set          32.531\n",
      "Kurt test p-value          0.000\n",
      "Norm test p-value          0.000\n",
      "\n",
      "Results for symbol MSFT\n",
      "--------------------------------\n",
      "Skew of data set           0.043\n",
      "Skew test p-value          0.387\n",
      "Kurt of data set          10.323\n",
      "Kurt test p-value          0.000\n",
      "Norm test p-value          0.000\n"
     ]
    }
   ],
   "source": [
    "for sym in symbols:\n",
    "    print \"\\nResults for symbol %s\" % sym\n",
    "    print 32 * \"-\"\n",
    "    log_data = np.array(log_returns[sym].dropna())\n",
    "    normality_tests(log_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Portfolio Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "uuid": "a0301e42-0104-4cee-bf30-aa224a2260a2"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas.io.data as web\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "uuid": "74a583b1-1e85-4efa-adf0-c70377606aa6"
   },
   "outputs": [],
   "source": [
    "symbols = ['AAPL', 'MSFT', 'YHOO', 'DB', 'GLD']\n",
    "noa = len(symbols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "uuid": "23729711-44d9-49a9-9c10-8d238be308cb"
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame()\n",
    "for sym in symbols:\n",
    "    data[sym] = web.DataReader(sym, data_source='yahoo',\n",
    "                               end='2014-09-12')['Adj Close']\n",
    "data.columns = symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "uuid": "2bd5a671-fb77-4c78-90d9-bef99c34af86"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f515989a650>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FQ9teeokf8N57j7f/6y9WkO3a8ai1atXCkV9riliOiCUSDUHEith8lJgbIbic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\njUaj0Wgs5P8D0NIiaQARsDMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51598518d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(data / data.ix[0] * 100).plot(figsize=(8, 5), grid=True)\n",
    "# tag: portfolio_1\n",
    "# title: Stock prices over time\n",
    "# size: 90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "uuid": "e7e3d4af-6b03-4b05-9bb2-69132aa9b96d"
   },
   "outputs": [],
   "source": [
    "rets = np.log(data / data.shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "uuid": "3416c8ac-7ee5-4c5d-a929-92aa2881382d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.267080\n",
       "MSFT    0.114505\n",
       "YHOO    0.196165\n",
       "DB     -0.125174\n",
       "GLD     0.016054\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.mean() * 252"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "uuid": "437bb447-a4b0-4ddc-97de-965d2cb6c9f2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>YHOO</th>\n",
       "      <th>DB</th>\n",
       "      <th>GLD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.072784</td>\n",
       "      <td>0.020459</td>\n",
       "      <td>0.023243</td>\n",
       "      <td>0.041027</td>\n",
       "      <td>0.005231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.020459</td>\n",
       "      <td>0.049402</td>\n",
       "      <td>0.024244</td>\n",
       "      <td>0.046089</td>\n",
       "      <td>0.002105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YHOO</th>\n",
       "      <td>0.023243</td>\n",
       "      <td>0.024244</td>\n",
       "      <td>0.093349</td>\n",
       "      <td>0.051538</td>\n",
       "      <td>-0.000864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DB</th>\n",
       "      <td>0.041027</td>\n",
       "      <td>0.046089</td>\n",
       "      <td>0.051538</td>\n",
       "      <td>0.177517</td>\n",
       "      <td>0.008777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLD</th>\n",
       "      <td>0.005231</td>\n",
       "      <td>0.002105</td>\n",
       "      <td>-0.000864</td>\n",
       "      <td>0.008777</td>\n",
       "      <td>0.032406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL      MSFT      YHOO        DB       GLD\n",
       "AAPL  0.072784  0.020459  0.023243  0.041027  0.005231\n",
       "MSFT  0.020459  0.049402  0.024244  0.046089  0.002105\n",
       "YHOO  0.023243  0.024244  0.093349  0.051538 -0.000864\n",
       "DB    0.041027  0.046089  0.051538  0.177517  0.008777\n",
       "GLD   0.005231  0.002105 -0.000864  0.008777  0.032406"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.cov() * 252"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Basic Theory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "uuid": "3d86ff5b-9de8-4cee-99ea-cc01e4697320"
   },
   "outputs": [],
   "source": [
    "weights = np.random.random(noa)\n",
    "weights /= np.sum(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "uuid": "9d9499c0-c033-4d4a-b2d4-e8ef064eb9ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.0346395 ,  0.02726489,  0.2868883 ,  0.10396806,  0.54723926])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "uuid": "c9ec5dc3-df96-4418-90fe-0e76ed7b006d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.064422321340550093"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(rets.mean() * weights) * 252\n",
    "  # expected portfolio return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "uuid": "40f68bdb-74ea-4f6d-9f93-c498a9a13167"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024930240314008388"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(weights.T, np.dot(rets.cov() * 252, weights))\n",
    "  # expected portfolio variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "uuid": "351e317e-5f22-474b-b1a5-1f2934f608a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15789312940723035"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.dot(weights.T, np.dot(rets.cov() * 252, weights)))\n",
    "  # expected portfolio standard deviation/volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "uuid": "689bff52-80a9-48ac-9fce-0855e2a763ae"
   },
   "outputs": [],
   "source": [
    "prets = []\n",
    "pvols = []\n",
    "for p in range (2500):\n",
    "    weights = np.random.random(noa)\n",
    "    weights /= np.sum(weights)\n",
    "    prets.append(np.sum(rets.mean() * weights) * 252)\n",
    "    pvols.append(np.sqrt(np.dot(weights.T, \n",
    "                        np.dot(rets.cov() * 252, weights))))\n",
    "prets = np.array(prets)\n",
    "pvols = np.array(pvols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "uuid": "81d7f822-79e9-41bb-94b1-649807788f2e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7f51596165f0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fUmFPE+ixA+oHwqBiUGIDVAyET+rCuUzovAG+bADtIqDlGhhdA345B2/vBqfJ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\nYAp0wzAMw/AApkA3DMMwDA/w/7NEyL22p9ezAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515971e890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.scatter(pvols, prets, c=prets / pvols, marker='o')\n",
    "plt.grid(True)\n",
    "plt.xlabel('expected volatility')\n",
    "plt.ylabel('expected return')\n",
    "plt.colorbar(label='Sharpe ratio')\n",
    "# tag: portfolio_2\n",
    "# title: Expected return and volatility for different/random portfolio weights\n",
    "# size: 90"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Portfolio Optimizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "uuid": "637b7c75-4c82-4eec-bf8e-2ff5516fd459"
   },
   "outputs": [],
   "source": [
    "def statistics(weights):\n",
    "    ''' Return portfolio statistics.\n",
    "    \n",
    "    Parameters\n",
    "    ==========\n",
    "    weights : array-like\n",
    "        weights for different securities in portfolio\n",
    "    \n",
    "    Returns\n",
    "    =======\n",
    "    pret : float\n",
    "        expected portfolio return\n",
    "    pvol : float\n",
    "        expected portfolio volatility\n",
    "    pret / pvol : float\n",
    "        Sharpe ratio for rf=0\n",
    "    '''\n",
    "    weights = np.array(weights)\n",
    "    pret = np.sum(rets.mean() * weights) * 252\n",
    "    pvol = np.sqrt(np.dot(weights.T, np.dot(rets.cov() * 252, weights)))\n",
    "    return np.array([pret, pvol, pret / pvol])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "uuid": "1fea02c0-d092-4a29-a9eb-2d4022985a62"
   },
   "outputs": [],
   "source": [
    "import scipy.optimize as sco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "uuid": "0124792d-7b7f-4f7c-ac42-367681a7145f"
   },
   "outputs": [],
   "source": [
    "def min_func_sharpe(weights):\n",
    "    return -statistics(weights)[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "uuid": "a634bb6a-ef2e-4a68-9414-b2e320b58296"
   },
   "outputs": [],
   "source": [
    "cons = ({'type': 'eq', 'fun': lambda x:  np.sum(x) - 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "uuid": "40e6c1b4-8fb8-4a8f-b2ba-36ffdaefc47b"
   },
   "outputs": [],
   "source": [
    "bnds = tuple((0, 1) for x in range(noa))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "uuid": "d689f1b3-c680-4a1e-865b-85a64b06c0f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.2, 0.2, 0.2, 0.2, 0.2]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noa * [1. / noa,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "uuid": "dda15397-398b-445e-be6a-11e7ed0e3c32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23 ms, sys: 0 ns, total: 23 ms\n",
      "Wall time: 23.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "opts = sco.minimize(min_func_sharpe, noa * [1. / noa,], method='SLSQP',\n",
    "                       bounds=bnds, constraints=cons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "uuid": "ee237054-2af4-4879-97d1-0ee77d84d9a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  status: 0\n",
       " success: True\n",
       "    njev: 5\n",
       "    nfev: 36\n",
       "     fun: -1.0630082691760654\n",
       "       x: array([  6.61851540e-01,   8.64635569e-02,   2.51684903e-01,\n",
       "        -9.28144822e-17,  -4.29479586e-17])\n",
       " message: 'Optimization terminated successfully.'\n",
       "     jac: array([ -1.82971358e-04,  -7.02321529e-04,   7.18027353e-04,\n",
       "         1.51409739e+00,   1.54873729e-03,   0.00000000e+00])\n",
       "     nit: 5"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "uuid": "e0a821b0-a330-4979-bec2-a9e731f11656"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.662,  0.086,  0.252, -0.   , -0.   ])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opts['x'].round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "uuid": "faae2c2f-382b-427b-9426-87085a339b76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.236,  0.222,  1.063])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics(opts['x']).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "uuid": "8aefec36-2e2e-49ea-ab30-75ccbcaa2b22"
   },
   "outputs": [],
   "source": [
    "def min_func_variance(weights):\n",
    "    return statistics(weights)[1] ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "uuid": "f84ed951-5f9b-4ec7-bb7c-330994bdb0b9"
   },
   "outputs": [],
   "source": [
    "optv = sco.minimize(min_func_variance, noa * [1. / noa,], method='SLSQP',\n",
    "                       bounds=bnds, constraints=cons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "uuid": "76321897-4b6e-4714-965a-79bdd489b846"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  status: 0\n",
       " success: True\n",
       "    njev: 9\n",
       "    nfev: 64\n",
       "     fun: 0.018288003377877962\n",
       "       x: array([  1.07601719e-01,   2.48940097e-01,   1.09310104e-01,\n",
       "         1.37422625e-17,   5.34148080e-01])\n",
       " message: 'Optimization terminated successfully.'\n",
       "     jac: array([ 0.03637059,  0.03644132,  0.03614312,  0.05221011,  0.03676902,  0.        ])\n",
       "     nit: 9"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "uuid": "031a4259-ecec-4cf4-85cd-98515fe15a6c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.108,  0.249,  0.109,  0.   ,  0.534])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optv['x'].round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "uuid": "1859a17c-e501-4a6f-88ae-16b9cf10b308"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.087,  0.135,  0.645])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics(optv['x']).round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Efficient Frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "uuid": "a49f6ebe-1552-400f-b97f-a26d1bd9fa2a"
   },
   "outputs": [],
   "source": [
    "cons = ({'type': 'eq', 'fun': lambda x:  statistics(x)[0] - tret},\n",
    "        {'type': 'eq', 'fun': lambda x:  np.sum(x) - 1})\n",
    "bnds = tuple((0, 1) for x in weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "uuid": "6ec20ed9-ec0d-4b86-bf1e-dbbcbd331b57"
   },
   "outputs": [],
   "source": [
    "def min_func_port(weights):\n",
    "    return statistics(weights)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "uuid": "75433eba-18d3-416a-95a9-c404293ef495"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.54 s, sys: 0 ns, total: 2.54 s\n",
      "Wall time: 2.54 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "trets = np.linspace(0.0, 0.25, 50)\n",
    "tvols = []\n",
    "for tret in trets:\n",
    "    cons = ({'type': 'eq', 'fun': lambda x:  statistics(x)[0] - tret},\n",
    "            {'type': 'eq', 'fun': lambda x:  np.sum(x) - 1})\n",
    "    res = sco.minimize(min_func_port, noa * [1. / noa,], method='SLSQP',\n",
    "                       bounds=bnds, constraints=cons)\n",
    "    tvols.append(res['fun'])\n",
    "tvols = np.array(tvols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "uuid": "b16fdf94-8324-481a-a360-ed2060b9f481"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7f5159434c20>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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btl3RM4UQHkB9YFWaY85CCDfbZxegKXA0w/qUBrWs6wXmTpD+zCL29Gm21qlD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\nCb2B8A4glmwpcDmPmy8ERDl7C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WG3ghAlfhPAeAAXADT5uyte2AdYBAHnfyfVCSSqkaIS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515971e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.scatter(pvols, prets,\n",
    "            c=prets / pvols, marker='o')\n",
    "            # random portfolio composition\n",
    "plt.scatter(tvols, trets,\n",
    "            c=trets / tvols, marker='x')\n",
    "            # efficient frontier\n",
    "plt.plot(statistics(opts['x'])[1], statistics(opts['x'])[0],\n",
    "         'r*', markersize=15.0)\n",
    "            # portfolio with highest Sharpe ratio\n",
    "plt.plot(statistics(optv['x'])[1], statistics(optv['x'])[0],\n",
    "         'y*', markersize=15.0)\n",
    "            # minimum variance portfolio\n",
    "plt.grid(True)\n",
    "plt.xlabel('expected volatility')\n",
    "plt.ylabel('expected return')\n",
    "plt.colorbar(label='Sharpe ratio')\n",
    "# tag: portfolio_3\n",
    "# title: Minimum risk portfolios for given return level (crosses)\n",
    "# size: 90"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capital Market Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "uuid": "b6eb023a-2407-49d7-986d-3d47e9c3ae45"
   },
   "outputs": [],
   "source": [
    "import scipy.interpolate as sci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "uuid": "cca0eb49-0c77-4186-a2df-06ebf56fa923"
   },
   "outputs": [],
   "source": [
    "ind = np.argmin(tvols)\n",
    "evols = tvols[ind:]\n",
    "erets = trets[ind:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "uuid": "c3bf6d9e-0548-4874-90fc-0555f05afd5e"
   },
   "outputs": [],
   "source": [
    "tck = sci.splrep(evols, erets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "uuid": "f37d6e01-0091-42d4-a9ca-1102f0fdb436"
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    ''' Efficient frontier function (splines approximation). '''\n",
    "    return sci.splev(x, tck, der=0)\n",
    "def df(x):\n",
    "    ''' First derivative of efficient frontier function. '''\n",
    "    return sci.splev(x, tck, der=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "uuid": "e0a3d820-a9f1-41fa-a5bf-46fe176fdb68"
   },
   "outputs": [],
   "source": [
    "def equations(p, rf=0.01):\n",
    "    eq1 = rf - p[0]\n",
    "    eq2 = rf + p[1] * p[2] - f(p[2])\n",
    "    eq3 = p[1] - df(p[2])\n",
    "    return eq1, eq2, eq3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "uuid": "2c23eced-0b5a-45cd-b677-f4eff5876f43"
   },
   "outputs": [],
   "source": [
    "opt = sco.fsolve(equations, [0.01, 0.5, 0.15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "uuid": "f4c2c1e9-aef7-4d22-9e1c-3b7ff5066830"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01      ,  1.01832869,  0.22606877])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "uuid": "3651a6fa-7740-43fe-a18a-1134cbed7b0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(equations(opt), 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "uuid": "b4e45542-14a5-4e96-9bbe-186873d9f429"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7f51592bb128>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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42tUO4AU0AVp4wuwc2rDPSYTiHtol3+YqdA2BHO7g6y6EhoayadMmCuXNTed2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Mxw+QI06a+6q9/eCi6+EBQJvRdARFPoHwnv1YO3KoNXEBit0HQYvLQU3toO/mXBzRe6T4fKT2G/\nmIQtKxPN2i8h+SRcTIDod8DsBuXbgFsgnD4E/XZB/z3QZyts/xrMXnBgFdhzn0lQJcixAV5Q711o\nNQH7mUMs+D2Oz/Z68sKbI/jrzz1w8RQ8Px9cA6DzXNWln51x7XlmXL7Amq27kX4xMPgcOTk5ZLWc\nAj6loVRTqNqLrLj9SPFoth89jxSpibwRT056CpndNkCVXmS1/IoE8WTZsmUcPHiIK8Uag94MgIS3\nIebwwXtqJwWhLT9QsvOYHlMcPfQCTk4OTJ4M770H589Dly7wwQfg7/+wNXPwbxk25B3Klg5j7cbN\n5FQqzZgxo4mNjSWqzpNkeRbHnnqWMsF+rF22GIPB8H9la9asyf5dO9myZQseHh7Uq1eP6JbtuBz9\nLlLvZQC0CwdSN3s3W0c2xV6tLTlHdmA7exKavwMrxsOgv1Tjm3QEPoiAgBBwCYB5n0D6JUhNIiXj\nMgRHwa+fQXY6uBeG9BTVqP/+HcRshjJPwamdkJ2KIeUMBmcrKSH1VEU1WghrCEuHwuJx4FYE+u7i\nwrS6BGpjODmwtGqgL50BN3+1l5+SBIVCwbukWodPKXDxB89QSNgHg0uAZxGI2w01XoP6A1W5/YvV\nj46eW0Cj5coTL3L8Iz91LfjBrqB3gfZT1Gj3L+pBw0FwcgfEbIQGI8E1UK3H5A4pp8EjWM2fj8Ve\nriuZzSaokfBf1YD9CwEFDLn7oSsKdr0LCxcuxGg0Yo75g7Ra/cHgjObPOZQKc4yX3xf+/n37n8Jh\n0Aswv/6q9soPHIA6ddQNVCpUuGOx+0pB3I+5IOnctm1b2rZtey3ftferJNd6A6n3Gtht7J7ckgkT\nvqRfv75/K1u4cGE6dOjA8ePHGTlyFH/t2YU0vL6vhM03DEvKSbasVnv72iYRDB46nIs/D4ciT6jG\n3G6H3QtR9FY4dxppNgKieqkVrPoIdsyFlEQIaw6n98A33aFCG1g3Afb8DD03gl9Z1dB9FkHTOpXI\nsGtZuWE82W3GQdp5lM1TEdFAsbpQbwBoNOhcfKhcsTgnz2fDpXj4sCJEtodDa8E9CJIOQNxOKBwJ\nsb9DyinIuKJ+IFy5qH4AtJ8FS/pCYEVwMsGiV8Hio8oAGN0RnRnCn4HaoyBxF3zTGMyFoEwv+GMp\npJ0DFDi7//qDDa4DX7eEmv0g+Sgc/AX6HFHPaXXgV0Et51MGZW4HpNYglPjfSDuwknnSAV3OeZRL\npzGOLY6TizfOmiy+W73sntpHQWrLD4T/uMvdYdALIPv2qYZ8+XIoUUIdL2/ZUvUUOni8OX78OFKz\ngZrRaEkvUY9DR4/fVv7gwYNUjqpNWqlO2Iu0hW9fAVc/KFQc86pPaPXuG5QvX57y5csD0LVrVwYP\nHsy4iVOxH90Mh1bDzh+R9j9B6mn4pbdq7AtHwqXTUKIxeJSAJS9B3bdgw1hcj67C6O5M4qmT4JPb\n88w1dL9s/pOKJX0Iy4znr/7OSE42EtIaovvCil5wJRmNmz+mjCROJbpDw9FQvBEcXQE7JqveAp0B\nwpvBl7XB6App50HvCjkZYPYGe64RPr4BrXsQMr87dkUHZTrAntmwbTIUq4fm9/HYM1Oh/ljQ6qFI\nLfAoCUXqQOXXgNcg4yLK575Yjv7E5RnHwegOh5ZAg4/h0gm4EI/Z4kLmH5Ow1R4GyYfhrzngEgip\nieguHSNwfSyJiUmkVR9JdsXXyAb0q7vSp4EXnTs/S2hoKEajMR9bzX+IjDuLPM44xtALEGfPQu/e\n6jamv/+u9sj37YNWrR6eMS+IY3gFWecnIiNx2jxZ7TmnX8K863uqV468bblhIz/hcsW+2Bp9hjQa\nA09+iGZyBywfVaFToyg8PNyoHFUfL78g6jZszvnz5xk7diyLF87DeWZrWDkamn8FRWpAeHt4og8s\negPmvwS7f4DUM7B1PFR8A3YtolhwMS4knmLONzMoV6ka2jVD1WlksVshZjXZzebw15F4LiQlIjoX\niHoPWs2FwKoQPQXNjplUSVlBx7atOXjgIKx9HzJTYdkbcGwN6F3RaRSeDXfi7X59WPvTbJxMVnWX\ntZY/Q5dD8MxOUDQ4H1qA/eR2fD09MCmZcGQVeJSCrRPgy0pEmWJQdEZIzh2/PrEGstNQji6GzBT1\n2MF5lCwVQfyxQ7zeLJxmRdOoWLEcxs3DcD6xmAD7CVYtXUTp1NVoRphgYjko9yrU+hy6xmDyK8vk\ncR/jYrVAUINrv0uWSymycuyUK1fuXxnzgtiW85V7XFjmccFh0B8hRIS1a9f+7XhmJnzyidobnzoV\nXnpJjWbv2xf0+oegqIOHxvSJ4wm7tB3jQF+cBgXyTMNqPPfcc7eVP38xBXEtcv2Ae1HKlimHh7sX\ncxdvoXX7zmzXNie54Vo2plakVv3G5OTk0LhxYy6dS0SnN0Dmpevl05IpdOUIbJkK5V+AQz9Du/UQ\n9QF0/I2EpPOsWrWKjRs3MuK9gZS+uBqGmGBWS/CrDBveJS05njh7mBpsl3HD9tFpZwkM8MfopGfK\nL4e5EDoUdIVgbFHIzoLOcdAlkZwK77Fk+Vo+HDWKOnXq4O/jAwZXCKih1uNZGtxDSA3sgLx4hdNh\nH6LROOHllIop9QiGyyeYOfkLNqz6laCiReG7J2HFa7CqH2SkIOkXYEJRmFgC85Z3WDhnFq6urnz6\n6ad8/PHHuLp44lfIl8Y1K7H3j9+pVq0af+7YQuzxozg56aFiXyhcB3Qm7JcTsVqtNG3cCOO2QZB2\nFs7+ifnARJpEXzfw98Lx48fZv38/KSkp/6qex4r/uEF/6PPm8jNRwOeh2+0iCxaIFCsmAiJNmojs\n3/+wtXLwsLHb7XLq1Ck5f/78HWVnff2NmP1ChZ47hZf+FEvh8hIaUUG0lYcKLTcIPlWFPqKmV+xi\n8Soqhw8fFhGR7OxsdcMT50Ah+nOh+kBB7yKaQlVF8aooOLkKFj+hr1xLZv9yoje7iNm/suit3tLl\nhV5StESY4GQVKo4QIkcJeneh+RbhqSPq8cpvCHU/Fowe0r59ezG5BQjdsoTuInTLFnQWoezrQm9R\nU7eLgsZJli5dKpmZmTJt2jRBaxCe+UPVo9sxtd5njl0rYwx4QlatWiWnTp2S9PT0a89nxYoVYrC4\nCUWbCO6lBMVJUHSCV1Wh1neiN7lIYmKiiIicOXNGXNx9hIofCY02ibF4K2nZttP/Pe/er/QVS2AF\nIWqUmEs0kDoNmorNZpO0tDTp8GxXMVpcxM3LT76cOPlftYF+/QeK0dlbXAIixc3TT7Zt2/av6ntQ\nkN/z0MdI3pJjHrqDB8nOndCvH2zYAOHh6nh5w4YPWysHjwKKouCfx2kMnZ99huTk83z8WUfEbufl\nni8wdcZsbGEt1Tnk6Ymqu1qrh+xUctIvcenSJU6cOEFQUBBunr5cDHkV4v8CrRFMvtjLvgMB0fBj\nGcg6C9s/grDOKMd+JO1sDJgKk5WhgLkcM2d9i6ubO1T6GEr1VpXSu8Hez6D+XAhqA6d3g0swBr2e\nypUrM2/xBlByX02KVk1xyyE7TY1Aj10CWgMtnuqGm7Men0I+4F0L5tUB12Jw8YgaGa/LXbEt+zIZ\n52IoVKjQ355bgwYNWLfqV0aPHcePv5zF1ng3OIfA7rfh6DcYXPxISkqiUKFCzJgxg9Q0G+wZCloj\nGZUnsvin58jOzsbJyQmACePHULP6HLZt/4OQkq3p3r07Go0Gk8nEnG++Ar7617//2rVrmTxjHhkN\nDpJh8ICTP9Cq3TOcij38r+su8DzGU9LygpL7ZfNYoiiKFLT7mz9/HUuW1OHrr8HLS51P/sILoHtE\nP73WrVtX4CJt/+s6t2jTiWUnA8mu9BGsaAtpZyC4BZb4hbhpL5OcfB5Fo6NMeCne7v8Knbv2RBdY\nk5TYbeBbB2p+A4qCcWtnQgz7OXg0juzMNFzcPLh0/iw4eYPBGyqMh7Pr4fBoqP4lFOuoKnB8Hhyb\nDVXGwM9V0Omd0NoyGPD2W+w/cJj5v6yDIs2geAc4Ng8l5msEQKMHsz+kxEDleeDfFE5+A3/0hoY7\nQOcMV47Bn+9ATgrkXIIijeH0erh8nKwrF68Z3psZPXo0A77Yiq36AvVA1iVY5I2zswuJZ+IYM3Y8\nQ4YOh/BPILgXXNwJW6LRSiqZ6WloH+BGKpMmTaLf+J2kl58KievAuybKPD3Z2VkPVI97QVEURCRf\nIn4URRFG5PF9Pzj/9HiYOMbQHxHS0mDYMOjcGb7/Ht58E44cgZ49H11j7qBgMnXiZxTLXINlYUkM\n5zZTPlDoW/0CjaoUI9lWnIza8aTXimNPYhGWrVjHvj3bmfJuBwp7m9DYs9QPgIQNKKeWEXcqgZzA\nfkjkKlKzPcESBuW/B89o2NIKdLnR57/3hVMr4fQq+P1ViF+K7qeyWI1alMxUKj9RleiG9TgccxxK\nfwAZGbD1LUg+RLFixfn0ow9wcjKAwRd8GqvGHKDws2DLgBVRsLq2atSzU+DyCSjWBbTe6CyB1K5V\n55bGPDY2lsjKtRkwaAi2Uyvh7Cb1xPmdoPcmOwc+++wzRo6ZBuigWG81AtW9ErhVpHF0k/tmRDdv\n3kz5SrUoHBxOz96vkZHx95DtPXv2MOPr+WQe+xn2jlK9LCfnEVg05JE35g+EjDymxxRHD/0R4cIF\nKFkS6tWDjz6C4OCHrZGDx5mcnByOHDmCyWQiKCgIRVFo3Lw9y063hoBOqtC5NZTLGsa2LSup37AF\nu/6KJcNmxZa6H3dPN/r07s5nM7aQErFSlV9bBCqvBkvugi8H3oDYCepiLiVHQ8JMEDtc3stnY0Yy\nYPAHZIQsAEsZlGNvoZxbiFanJ9sYBFUXwKGPIX4hij2Vb2d+yblzF/jk0y84lZSKNNgHTq6QtAZ+\n6wQ1E+D0NDjcH43ZE509FWdnZ7IzrmCza0i/kkxQ0VAWzP+aCrmLNdjtdkqWKk8sHbAFvALn18K+\nZ8EvGpLWQ9lv4NIOwgwLOSDPwaF3ofZWcAmHnCsY1kewask31KhR41//HkeOHKFCZHWuBH0OltIY\n44fQuo4X3307/ZpMTEwMFSKrc9lvCBiD4Uh/9NpUrEaFVct/uXZfjzL53kMfmMf3/ShHD/2+oyhK\ntKIoBxVFOaIoytu3kRmfe36PoigV7qZsQcLdHZKTFebPVxzG3EG+o9PpCAsLo2jRotf2EAgvVQLD\nhaWq0RXBKXkJYaVKMHHiJHYe0nCl9F/Yym5HKTaKUqERREVFITkpai8RQHGC7IvXL5J9AXx7gqEw\nBPaESlvhid9RjAG88eZgMpybgFstcHJHSozHnp1KdvlYdVrHslA4fxKKfom4taHXy2/Su3cP4o4f\n4KUXOmJaVxrTlhqqFyDiO7XX7N8djZLF5LGD2LJ+GSeP7kWvN3DFdyT2Kpc4rhtI/QbNSE1NBSAx\nMZHTp89gKzJI9SQUaoXiUgGycqDKJvCORmO7gJuzFV3aXijyImyoBb+1QbMmjI5to4mKirr50d4T\nS5cuJcerHfh2BOeyZBT/ijlzZqPT6fENKM7atWuZO3ce6R5PQ+E+4N0Myi7ErFc4efzQNWMeHx/P\nM892p0btpgx7fyQ5OY9zSPct+I8v/frQDLqiKFrgCyAaKA10UhQl7CaZJkAJESkJ9AAm5rWsgwdD\nQZwH69D51gx9bxCl3I9h3VYW552VCJQVjBs7ikOHj5Fuqn8tUE1cG3Ls2DFq165NEW8wHOgMcVNx\nctKg3dMaYifCH0/B2WUQ+DrYM+HkeDWALm4iknkBm/84SI+9/jGQdhB07rkLvAwBjQFCfwCP5lB8\nOunZRnbt2gXAF+NHs23Tcob3a43Z6gkuldU6UrZhMpl54YUXiIyM5PDhw2TaXcCnK2hNUOhp7Dp/\n9u3bB4CLiwu27DTIPK2WT16BJvMkJK+F5JVwaBD22Em8884A9OcWw+nFYKkLiato0bAyM6Z/ee1j\n6N9iNpvR5iRdP5CViCgmbFUukejxJc1bticlJQVFbjDQkoPYc7BYLABcvHiRyBYPvKMAACAASURB\nVMo1mbvRl82JPfn4yzV06db7vuhXYLDlMT2mPMzR2cpAjIicAFAUZQ7QEjhwg0wLYBaAiPyuKIqb\noii+QHAeyjpw4OAusFqtbP9tHTt27MBms1GpUiWMRiNVq1Rk1rzPScvpDloXnM5NoVJkRQwGA1s3\nreKjj8dwOGYLtXv1w9/flx8WLWXJ4k1c8uujuobDF6PsrY8u9h1ybApSYjmYK8D5WbCrDopzWeTM\nt1B4MOSkobm4FLs6s+iabnq90/8Zz4iICMLDwzlw6ARz5pdF6xpBzvnfmPPdLGJiYjh06BBWq5Xs\ntDOQnQxOnpBziawrJ/HyUveKt1gsDB06lBEf1yDbozXaC7+SbUuBgE/h7O+gMePk2ZyVK1eCxgxh\nu0BrhqxTLFlaihYtO7J95x8UDizMV9PGERsby7z5v+DqYiEkJBi73U6tWrXy5Apv3749748YTfbh\n7mQbSkPsx1BkuPoh4t4I7aUogoOLYk4ZSuoJf8QYjPn0cNo/1fpaHcuXLydNCccW8AEAaa51+f47\nL44cOUFmVhbdu3bk5Zd73bePkEeS/5hD4mYe2hi6oijtgEYi8mJu/lmgioj0uUHmF2CUiGzJza8C\n3gaKAtH/VDb3+C1v7nb3fLuG/qDkb94+9WHr45B3yIM61tyz92vMmjUTrc5M8WJFWbPqFwoVKnTb\n+vft20fN2g2xGUtjz0ygXGl/Nm9YcUv5IkWKEhefgGAFuYKvnw8eHl7s37sjT/pv27aN06dP07p1\n61vKWzxCyHFuiC51NW1b1OTNN/pQsmTJ26/QFhEDxuIA6E/34PmmMG/JcS6dXnVLcSVwMobzb6No\nXEg394OLX4DGFTJ25kn/a/XcztBGZWA5UJ6liybj4+PD0OEfk3z+Eit/XXBr+Wq59dtSYZs7BM0E\nnQ8cvfW81wfZ3vJ9DL13Hu3ZxMdzDP1h9tDz+iWRbw/9qhszr9OB8lv+bnnU9HfIP57yiqIwZdJ4\nmjauT2ZmJu3atUNzhz15w8PDmfnVRPbv30/16tWJiopCd5vpGifPtQBmg+tYkIukprzP9t83Urhw\n4fui/+Tx75KUlMT8+Xv4fvZ3LPpxPRbT7QdSzWfak+b2DkrKr+jSfqBXr1V8Pzf6tvLi3oPMhMGI\n59tgSwCtLwSth4P/HHWeV/2th2tQt2Y5bDYbZ86c4fvcQLnbGVDdqYHkmKqgiR+C3VgKPJ79x/rv\nVp+7lR86dCgAJ06cyFO9/4p/MT6uKEo08BmgBaaJyEe3kBkPNAbSgC4isiuvZR8ID2tFG6AqsOyG\n/EDg7ZtkJgEdb8gfBHzyUjb3uBQkUD9yHrYad8XatWsftgp3jUPn/OdW+v7xxx9itvgK7ocFpyaC\n60zBT9RkHSZYeguFRVw8yt/Tyme7d+8WF49woYRcS65eVWTjxo3y008/icU1XAi4IBQWUTzGS9ly\n1f+ms91ul3HjvpBadZpJ23ad5dChQyIisnHjRvELKCEarU5CS1cSrU4vhJ0VyogQYRO0rkKxA4LP\nF4JbTyFM1BSaLqCVipVq3/X9bN68WT777DNZsGCB2Gy2W8rc/JxPnTolnZ/vITXrNJMq1WoLvu8I\nFURNxX+VsIiqd63H/YT8Ximus+Qt3aQHqiGOQfX+OgG7gbCbZJoAS3P/rgL8lteyDyo9zB76DqCk\noihFgdNAB6DTTTI/A68AcxRFqQpcFJFERVGS81C2wCEiBTJgy4GDvLBz507QR4O2JHAZtAHXT2oD\nIDsGco6RlR57x955cnIy3377LcuWrcPLy5uXXupKREQEkpMEaWvAXA/St5KdEUNoaChTp04lQ2kK\nGjcAxPg0hw+/87d6FUXh1Vdf5tVXX/6/4zVq1OB0/BFEBEVR6P/mICZNr88VQ2eMtk24eHtw+WJ3\n0gy9IGU+uD4PxnKQNBiM1YmPj7/r51W9enWqV69+V2X8/f35euZkQN1pr1LlmqQlGhGtJ+aLHzB0\n2qd3rUeB4t7H0B+LmK6HZtBFJEdRlFeA5ahfONNF5ICiKD1zz08WkaWKojRRFCUGuAJ0/aeyD+dO\n7i8FbQWzgqYvOHR+ENxK36CgIJScsaBJA30bSHkD3GaCpEHqYExWX7hUhdGjP8TX1/e2de/Zs4ca\nNepz+XIW6N4FRcuChS35del8flw0h1atO2JLcUIhg3lzv8Hb25uQkBCMMpIr9ndBY4GMHwkODr2j\nzjdz1c39yccjqBRZlo2bfqdYcC169vyWjz7+lLnzxnFJXEiKawD2bDDVRWcuQdUqgbet8/z585w6\ndYrg4GCsVusddcirzqVKleL3resYPeYLrqTF0K3LNKKjbz908Fhw7y73ACDuhnw8ai/8TjIBgH8e\nyj4QHAvLOHDg4IEgInTu3IMff16LximM9NQNuLp6YLVa6fHiM1SoUI6QkBCKFy/+j/WUKVOdvfu0\noOsIutyedM4Mnqz9CytXLiQrK4uEhAR8fHwwGAzXrv18l14sWLAYJ2MAWs6wft2vRERE5Mt9vvX2\nED79dDRarZ5y5SL5dekPeHp6/k32q69m8fIrr+Ok90NsZ/nxxznUr1//vuv0qJDvQXGt8/i+X/T/\neiiK0pY7BFrnBml/KCKbc/N3FaT9IHAY9EeMgrbOeEHTFxw6Pwhup6+IsHXrVhISEoiMjCQoKOiu\n63Zz8+dSSiXQtQNd7taxth+oUXkmGzcuvm05EeHgwYNcuHCBMmXK4OzsnCed75X09HQyMjJwd3e/\n5fnjx48THlGZdGUTaEIhZy1WXXvOJsVx7tw54uLiCA0NxcPD47bXKGjtIt8NevPbvO/PrYPkddfz\nh4fdbNCrAkNFJDo3PxCwyw3BbYqiTALWicic3PxBoDaqy/0fyz4oHKuEO3Dg4IGhKMpdjwvfTIUK\nkWzYZMCePRgUD0CLQfMGL730z+9PRVEIC3tw60+ZTCZMJtNtzx88eBC9oQLpObmuf11dRMwMHfoB\n48ZNxGAoTk7OcRYt+o4GDf7d3un/GW43hu5WR01XOTzsZonHIqbL0UN34MDBfWXLli306NGXs2fP\nUq9ebaZMGf+33vDdYLfb+fDD0cybtxgPDzfefvtlXn99MEePHSE7W4u3lxejRw/huec656m+Xbt2\nMXr0BDIzs+jZs/M9GUu73c6qVatISkqiatWqlChR4q7rOHLkCOXK1yBd2QGawmDbjtH+JIpiJj1r\nOyiBIBuwmtpy/vzp2+4UV5DI9x76k3l836/6ux6KojTm+tSz6SIy6saYrlyZqyuUXgG6isgftyt7\nX27qLnEY9EeImxeWceCgoHH8+HHKlHmCK1eGAqUxGD6nXj0Nr73WgwkTZqDTaXnzzVeoVq1anut8\n663BTJiwkrS0AUAsFstIdu7chNVqxWq14urqmue6du3aRc2aDblypR9gwWz+kO+/n0SLFi3yXIfd\nbqdZs/Zs3HgEKI3dvor582fRpEmTPNdxlTFjxvPOkPcxGEPJzjzEq6++yJcT95Fy5edrMiYnH2Ji\ndv1tL/eCSL4b9Jp5fHdufDwXlnlo89AfRKKAzenGMQ/9geDQOf+YMmWKmM3tBL4XOC5wQDQanZhM\nPgIjBN4Vk8lTtm7d+reyZ8+elSZN2oqHR6CUKVNVdu7cKSIi7u4BAtsFkgWSRafrKSNHjrwn/Z57\nrofABwJpuek7iYysKyJ5f8YLFy4Uq7WSQJaoi9FvEA+PgHvSR0TkxIkTsn79eklISJD9+/eLyewj\naI8LOhE0q8TFpZBkZ2ffsmxBaRdXIb/noVeTvKV81ONhJscYugMHDu4bFosFRUni+kKQiYhYSE8f\nhLrAFqSn2xkz5kvmz696rZyIEB3dmj//LEZ29izOn99B3bqNOXRoT+4+39c3sVaU9Ht2P2dn5wA3\njmub73pHstOnT5OTE4m6hghAFS5eTMBut99xBb1bERQUdC040MfHhw9Hvcvbb1dAry+MXRL46ad5\nt11lz8FNPMY7qeUFh8v9EcLhcndQ0ElLS6NChShiYwPJzAzDbJ6Hp6eVuLiXgKtj1d/TuvVhFi78\n9lq5Cxcu4ONTmOzsnVzdBNLZ+SVmzOjF8eMnee+9iaSlvYZGcwIXl9ns3budgICAv13/Tqxbt44m\nTTqSnj4a1eX+JuPHD+KFF7qRlZXF2bNnKVSo0D9+MOzcuZOaNZuRnr4OCEGr/YCIiOXs3r3prvW5\nHerWrqcpXrw4Li4u963eh02+u9wr5PHduevxdLk7PvscOHBw3zCbzezYsYFJkyZx+nQiTz45gcuX\nL9Ot2xukpdmBTEymL+jTZ+7/lTOZTIjYgPOAF2BDJBGr1Ur//n3x9S3E/PlL8PJyY8iQLfdkzEFd\niGXhwpkMG/YpWVlZvPzyYLp168qyZcto1+5p7HYdTk7w88/zqV279i3riIyM5IsvRvHSS5Ww2XII\nCSnDL7/cZqOUPBIfH0+HDt3YvXsngYFBzJ49hUqVKv2rOv+T/Md3W3voPv/8TBSw8WgcY+gPBIfO\n+c/N+s6dO1eqV28ktWo1kWXLlt2yzKBB74nZXEKgr5hMtaVy5dq3HTu+nyQlJYnZ7CEwROCwwFfi\n7Owtqamp/1jOZrPdUSYv2Gw2KVmynGi1bwj8JTBRXFwKSWJi4h3LFrR2QX6PoV9dQ/9OyTGG7iC/\nEXGs5e7g8aR9+/a0b9/+H2VGjBjKE0+UZ/PmrRQt+hTdu3d/IGPHhw4dwsmpKBCSe6QG4JYbsV/m\ntuU0Gs0dl2rdt28fSUlJlC1b9v9Witu0aROdOnUjISGOkJBwTp6Mx2brj7q5ZGtgHtu3b6dp06b/\nWH9cXBxffvklzs7OtG3bFrPZnIc7foxxjKE/vuO1BW0M3YEDBw+e2NhYSpWqSEbGj4AvcBKDoQ3x\n8Ufx8vK6pzpFhN69X+Obb+bh5FQEu/0Ey5b9SPXq1Tlz5gwhIWW5fPkDoAqK8hXqNOedgDeQhcVS\nn+XLZxAVFXXba6xbt46mTdsh0gCN5gyBgSns2LHhrteDf5Dk+xh64Ty+7+MezzH0uw/JdODAgYPH\niKCgIN57byBmcztcXHphMnVk7NiP79mYA6xatYpvv11CWtpCLl2aRmrqO7Rt+wwA27dvR6MpA9QD\nLIi8glZrxGxuhaKMxGJ5ipo1I+44V79Hj76kpY0kPX0UV67MIDbWl2nTpt2zzo8FOXlMjykOl/sj\nRkFbm7mg6QsOnR8EBU3fAQP64+Pjibu7O2FhYYSGht650D8QExOD3V4RuNpbrkFiYl9sNhuenp7Y\nbLFAFqAHEtFocvjmm4/Zt28fRYu+zNNPP33HKXDJyeeA9NycQkZGKRISkv6V3gWe/7jL3WHQHThw\n4AAIDg6+bx8hZcuWRVFGAGdR3eiLCQ4OQ6vVUr16derXr8Tq1U+TnV0BnW417777Pm3atKFNmzZ5\nvkb9+nX58ccfyM5uAJzGbJ5P/fpf3Rf9Cyy2h63Aw8Uxhu7AgQMH+cDw4R8yYsRInJzcMZmENWuW\nXtuu1W63s3DhQk6ePElkZORtp8j9E6mpqXTo0IUVKxZjNFr56KMPePnl3vf7Nu4r+T6G7pzH933q\n4zmG7jDojxCOhWUcOHi8SE5O5ty5cxQtWvTa3uz3GxG59u541Ml3g27K47sz/fE06A8lKE5RFA9F\nUVYqinJYUZQViqK43UYuWlGUg4qiHFEU5e0bjg9VFCVeUZRduSn6wWnv4EYK4jQ7h875T0HTF/JH\nZ09PT0JDQ/PNmK9bt67AGPMHQnYe02PKw4pyHwCsFJEQYHVu/v9QFEULXN2qrjTQSVGUq5sZCzBW\nRCrkpmUPSG8HDhz8RxARPv/8C2rVasRTTz3N4cOH81TObrczY8YM+vZ9g+nTp2Oz/ccHdh8k//Eo\n94ficlcU5SBQW9TN4X2BdSJS6iaZasB7IhKdmx8AICIfKoryHnBZRMbc4ToOl7sDBw7uiUGDhjBu\n3GzS0uqhKEk4O29h377dBAYG3raMiNCxY2cWL95BWlo4ZvN+GjUqx4IF3zt60jwAlzt5fXc6XO73\nEx8RScz9OxHwuYVMABB3Qz4+99hV+iiKskdRlOm3c9k7cODAwb0yYcJE0tKeBcoh0oDMzDDmz/9f\ne3cfZEV15nH8++NFlhcJL4oOBjPGjaAVDb6QJRFXEhODoVSMiSayrsaYcqt2NRitgHHdsJr4VhVK\nYiomq9ECNkLcFVSMEtAiiSsiwTCABWgIDhrxJb6MhRlJWPbZP/pcaS53ZvrOnb7dt+f5VN2a7r7n\ndD99Zu6c231On/NfnebZtm0bS5c+Snv75cAZtLdfzrJljye+uneuFqk9tiZpBdGwS+Wuja+YmUXf\nrPbT2VetO4Drw/INwPeBr1VKePHFF9Pc3AzAsGHDGD9+/PuPppTazPKyXi7reJKst7S0MGPGjNzE\nk2S9tC0v8SRZL48963iKFi/Abbfdts//h927/wpsJ3rsDPbsaWPr1q3vn1el/W3dupV+/YYAA4Dn\nAOjf/0B27tzZKz9/LS0ttLW1AdDa2opLWRYDyANbgEPDchOwpUKaicCy2Po1wMwK6ZqBjR0cxxpN\no0220GjxmnnM9dBo8ZrtH/OsWdfaoEEfNrjUpLNt6NCR9uKLL3a6j127dtmYMUda377nGHzP+vQ5\n1w477Ahrb2+vS8x5R9qTs/DXhK/04sjylVUb+q3Am2Z2S2gbH2Zms8rS9CP6insasANYA3zFzDZL\najKzV0K6K4EJZnZBheNYFufnnGt8Zsbcubdz//0PM2rUSG68cXaiEeS2b9/OhRdeyubNmxk7diwL\nFtzFEUccUYeI8y/9NvT2hKkHJY5D0gjg58CHgFbgPDNrq5DubmAq8LqZHRvbPhu4lGiUIYBrLKWO\n3FlV6COA+4DDiRWQpNHAnWY2NaQ7A7gN6Av81MxuCtvnA+OJbsu/AFxme9vk48fxCt0553Ii/Qr9\nnYSpP1BNhX4r8IaZ3Roenx5efgEa0p0CvAvML6vQvwPsNLM5CYPrtkw6xZnZW2b2GTM7ysxOL33b\nMbMdpco8rD9qZmPN7G9LlXnY/o9mdpyZfczMplWqzBtVvO2xETRavOAx10OjxQseczG8l/BVlbOA\neWF5HjCtUiIzewJ4u4N91KVHvc+25pxzDWLPnj3MmHEVw4ePYtSow1i8eEnWIeVMKiPLJHkqqyt1\neSrLh351zrkGcd11s5kz5z9pb58K/IVBg5Ywf/6POPfcc7MOLZH0b7m/0MG7q8OrZO4+cXTxVNY8\nMxseS/uWmY3oIIZmYGnZLfdR7G0/vwFoMrOKT2XVyiv0HPGBZZxznRk3bjzPPXcCUf8sgLWcf/4w\nFi1akGVYiaVfoSd93v+oatrQtwCTzexVSU3ASisbCC2WtpmyCr2a92vlt9xdTRqxDc9jTl+jxQuN\nEfPw4cOIN9P26dPKQQdVvFjspVIZ+/Uh4KKwfBHwQDWZw5eAknOAjdUGkJRX6M451yDmzLmJwYNX\n0rfvcg444GEGD36ZmTOvzjqsHEmlDf1m4LOSngc+HdaRNFrSL0qJJC0EVgFHSXpJ0lfDW7dI2iBp\nPXAqcGU3T65Lfss9R/yWu3OuK5s3b2bx4sUMGDCA6dOn09TU1HWmnEj/lvvqrhMCMDG1OLLkFXqO\neIXunCuy9Cv0/0mYelIhK3S/5e5q0gjtjuU85vQ1WrzgMRdD754QPbXJWVz1zMw/oM45120Fnuw8\nAb/l7pxzri7Sv+X+SMLUny/kLXe/QnfOOVcQvfsK3dvQc6bRbrk3WrzgMddDo8ULHnMxeBu6c845\nVwBVT7xSKN6G7pxzri7Sb0O/J2Hqr3obukuXP4funHO18Db0upM0QtIKSc9LWt7RdHKS7pb0mqSN\n3cnv0teIbXgec/oaLV7wmIuhd7ehZ9UpbhawwsyOAh4P65XcA0ypIb9LWUtLS9YhVM1jTl+jxQse\nczGkMjlLw8iqQj8LmBeW5wHTKiUysyeITy1UZX6Xvra2tqxDqJrHnL5Gixc85mLo3VfoWbWhH2Jm\nr4Xl14BD6pzfOedc4RT36juJ1Cp0SSuAQyu8dW18xcws6p3YPbXmd7VpbW3NOoSqeczpa7R4wWMu\nBn9srf4HlbYAk83s1TD5+0ozG9dB2mZgqZkdW21+r+idcy5f0n1sLfs4spTVLfeHgIuAW8LPB9LI\nX8RfmHPOuf35//vsrtBHAPcBhwOtwHlm1iZpNHCnmU0N6RYCpwIjgdeBfzOzezrKX/cTcc4553Ki\n0CPFOeecc71Fw0/OUsUgNVMkbZH0e0kzY9tnS/qjpHXhVem5956Is+Lxy9L8ILy/XtLx1eTNYcyt\nkjaEMl2Tl5gljZP0lKRdkq6qJm8O481rGU8Pfw8bJD0p6bikeXMYb17L+OwQ8zpJz0j6dNK8OY05\nk3IuHDNr6BdwK/CtsDwTuLlCmr7AVqAZ6A+0AEeH974DfDPlGDs8fizN54FHwvLfAauT5s1bzGH9\nBWBEnf8WksR8MHAS8F3gqmry5inenJfxJ4APhOUpWf4t1xJvzst4cGz5WGBrVmVca8xZlXMRXw1/\nhU6yQWY+TvTH02pmu4FFwNmx99PuTNHV8SF2Hmb2NDBM0qEJ8+Yp5viYAPXupNJlzGb2JzNby/6j\nS2RRzrXEW5LHMn7KzN4Jq08DH0yaN2fxluSxjP8cWx0CvJE0bw5jLun1ndpqVYQKPckgM4cBL8XW\n/xi2lVwebgX9tKNb9jXq6vidpRmdIG8aaokZwIDHJK2V9PXUokweT5p5u6vWYzZCGX8NeKSbeXtC\nLfFCjstY0jRJm4FHgSuqyZuCWmKGbMq5cBpitjXVPkhNZz3/7gCuD8s3AN8n+lD3pKQ9D/P0DbXW\nmCeZ2Q5JBwMrJG2xaCjfNNXSwzOL3qG1HvNkM3slr2Us6VPAJcDJ1ebtQbXECzkuYzN7AHhA0inA\nAkkVx/Kok27FDIwNb2VRzoXTEBW6mX22o/cUzcZ2qO0dZOb1CsleBsbE1scQfYPEzN5PL+kuYGnP\nRJ3s+J2k+WBI0z9B3jR0N+aXAcxsR/j5J0lLiG7Jpf0BTRJzGnm7q6Zjmtkr4Wfuyjh0LLsTmGJm\nb1eTt4fVEm+uy7jEzJ6Q1A8YEdLl9f/F+0oxSxppZm9mVM7Fk3Ujfq0vok5xM8PyLCp3iusH/IGo\nw8YB7NsprimW7krg3hRi7PD4sTTxDmYT2duRqMu8KZVrLTEPAg4My4OBJ4HT8xBzLO1s9u0UV/dy\nrjHe3JYx0fgQW4GJ3T3fnMSb5zI+kr2PHZ8A/CGrMu6BmDMp5yK+Mg+g5hOIvpU+BjwPLAeGhe2j\ngV/E0p0BPBc+uNfEts8HNgDriUacOySlOPc7PnAZcFkszQ/D++uBE7qKvQ5l262YgQ+HD3QL8Gye\nYiZqunkJeIdoJr8XgSFZlXN34815Gd8FvAmsC681Wf4tdzfenJfxt0JM64iuZCdkWca1xJxlORft\n5QPLOOeccwVQhF7uzjnnXK/nFbpzzjlXAF6hO+eccwXgFbpzzjlXAF6hO+eccwXgFbpzzjlXAF6h\nO1cHkj4m6Yxu5PuVpBN74PitkkZ0kebbZetPhp/NkjaG5ZMkzQ3Lp0r6RK2xOed6hlfoztXH8UQj\n61XL6Jkx0JPs45p9MpidXJ7AzNaa2TfC6qeAT/ZAbM65HuAVuut1JP2DpKclrZP0Y0l9JE0IM+4N\nkDRY0rOSjpE0WdJvJD0saYukOyQp7Od0SaskPSPpPkmDw/YJkp6U1CJptaShRBMAnR+O+aVwjLtD\nHL+TdFbIO1DSIkmbJC0GBlI2AY6kKZLui61PlrQ0LH9F0gZJGyXd3MH5LwmzWj1bmtkqpB0Y4lsQ\ntr1bIe9kSUslfYhoFLArQ/yTJG0LY4ojaWhY71vTL8s5l1hDTM7iXE+RdDRwHvBJM9sj6UfAdDNb\nIOkh4LtElegCM9skaRQwATiaaNjVZcAXJP2aaLa/08zsPUkzgW+GivHnwJfM7BlJQ4D3gOuAE83s\nihDHjcDjZnaJoil7n5b0GPBPwLtmdoykY4Hfsf/V9WPATyQNNLP3gPOBhZJGAzcTjZPdBiyXdLaZ\nPViW/xIze1vSQGCNpP82s1mS/tnMjo+l6/Cq3sy2S/oxsNPM5oRz+hUwFXgQ+DJwv5nt6fw34pzr\nKV6hu97mNOBEYG240B4IvBreux5YS1QBXx7Ls8bMWgEkLQQmAbuAY4BVYT8HAKuIpoPcYWbPAJjZ\nuyGf2PdK+3TgTElXh/UBRJOEnALMDXk3StpQfgJm9r+SlgFnSbqf6Fb+1cBngJVm9mY45s+Avyeq\nYOO+IWlaWB4DfARY01mhdSJ+TncRjdf9IHAxcGk39+mc6wav0F1vNM/Mvl1h+0FEsz31Jaro28P2\n+JWqwrqAFWZ2QXwH4aq6kkpXu18ws9+X5S8doyuLgH8B3gJ+a2Z/llSKqzzW+P4nE32pmWhmuySt\nBP4mwfG6ZGarQge6yUBfM9vUE/t1ziXjbeiut3kc+KKkgwEkjZB0eHjvJ8C/AvcCt8TyfDxUVH2I\nbtc/AawGTpZ0ZNjPYEkfAbYATZJOCtsPDO3IO4EDY/v8JXBFaUVS6Vb3b4ALwraPAsd1cB6/Jrq1\n/nWiyh3gt8CpkkaGY345pIsbCrwdKvNxRNPeluwutYEnVH5OEM1e+DPg7ir245zrAV6hu17FzDYT\nVdrLJa0nmnK3SdKFwF/MbBFRO/SEcKVpRBXlD4FNwDYzW2JmbxDdVl4Y9rMKGGtmu4natG+X1EJU\ncQ8AVgLHlDrFATcA/UMHtmeBfw8h3gEMkbQpbFvbwXn8H/AwMCX8xMxeAWaFY7UAa81saSlL+LkM\n6Bf2fxPwVGy3/wFsKHWKY9+r+0rLS4FzwjlNCtvuBYYDCyvF7ZxLj0+f6lwnQqV+lZmdmXUsjUDS\nF4EzzeyirGNxrrfxNnTnOtdTz4EXnqTbgc/RveftnXM18it055xzrgC8gNu0qgAAADJJREFUDd05\n55wrAK/QnXPOuQLwCt0555wrAK/QnXPOuQLwCt0555wrAK/QnXPOuQL4f68e9NUsDA0VAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f515971e590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.scatter(pvols, prets,\n",
    "            c=(prets - 0.01) / pvols, marker='o')\n",
    "            # random portfolio composition\n",
    "plt.plot(evols, erets, 'g', lw=4.0)\n",
    "            # efficient frontier\n",
    "cx = np.linspace(0.0, 0.3)\n",
    "plt.plot(cx, opt[0] + opt[1] * cx, lw=1.5)\n",
    "            # capital market line\n",
    "plt.plot(opt[2], f(opt[2]), 'r*', markersize=15.0) \n",
    "plt.grid(True)\n",
    "plt.axhline(0, color='k', ls='--', lw=2.0)\n",
    "plt.axvline(0, color='k', ls='--', lw=2.0)\n",
    "plt.xlabel('expected volatility')\n",
    "plt.ylabel('expected return')\n",
    "plt.colorbar(label='Sharpe ratio')\n",
    "# tag: portfolio_4\n",
    "# title: Capital market line and tangency portfolio (star) for risk-free rate of 1%\n",
    "# size: 90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "uuid": "f2e04c2a-434a-442d-bce2-0f7be60c6e80"
   },
   "outputs": [],
   "source": [
    "cons = ({'type': 'eq', 'fun': lambda x:  statistics(x)[0] - f(opt[2])},\n",
    "        {'type': 'eq', 'fun': lambda x:  np.sum(x) - 1})\n",
    "res = sco.minimize(min_func_port, noa * [1. / noa,], method='SLSQP',\n",
    "                       bounds=bnds, constraints=cons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "uuid": "78362c70-0acf-4f13-9a7d-04adbc7de43a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.687,  0.057,  0.256,  0.   ,  0.   ])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res['x'].round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://www.pythonquants.com\" target=\"_blank\">www.pythonquants.com</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a>\n",
    "\n",
    "<a href=\"mailto:analytics@pythonquants.com\">analytics@pythonquants.com</a>\n",
    "\n",
    "**Python Quant Platform** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://www.derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://shop.oreilly.com/product/0636920032441.do\" target=\"_blank\">Python for Finance @ O'Reilly</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python2",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
